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个人简介

姓名:王标

年龄:

职位:

邮箱:

详情介绍


王标,博士,副教授,硕士生导师,自动化教研室主任。研究方向:数据挖掘,机器学习,智能计算,无人系统等。联系方式:13889100448wangbiao_auto@sau.edu.cn.

教育经历:

2014.09-2019.09,东北大学,控制理论与控制工程,博士

2012.09-2014.07,东北大学,控制理论与控制工程,硕士

2008.09-2012.07,东北大学,自动化,学士

主持或参加的科研项目:

纵向课题

1. “对抗条件下有/无人协同意外事件响应与不确定性实时规划”,航空科学基金,27万,在研,主持。

2. “面向工业过程的异常数据检测算法的研究与应用”,校博士启动基金,5万,结题,主持。

3. “非完备信息下无人机自主集群协同对抗决策与导引技术研究”,国家自然科学基金面上项目,65万,在研,参与(排名2)。

4. “无人机智能决策技术研究”,国防科工局项目,80万,在研,参与(排名3)。

5. “无人系统集成”,总装备部项目,60万,在研,参与(排名3)。

横向课题

1. “复杂任务域随机化鲁棒策略训练软件”,129.2万,主持。

2. “云南太标70吨电炉伸缩氧枪智能控制技术”,15万,主持。

3. “预警探测作战数据模型评估软件”,13万,主持。

4. “网络巡飞智能决策VPX板卡开发”,150万,参与(排名3)。

已发表学术论文情况:

1. Wang, B., et al., Selective Feature Bagging of one-class classifiers for novelty detection in high-dimensional data. Engineering Applications of Artificial Intelligence, 2023. (中科院一区TOP).

2. Wang, B., et al., Boosting the prediction of molten steel temperature in ladle furnace with a dynamic outlier ensemble. Engineering Applications of Artificial Intelligence, 2022. (中科院一区TOP).

3. Wang, B., et al., Dynamic selective Gaussian process regression for forecasting temperature of molten steel in ladle furnace. Engineering Applications of Artificial Intelligence, 2022. (中科院一区TOP).

4. Wang, B. and Z. Mao, A dynamic ensemble outlier detection model based on an adaptive k-nearest neighbor rule. Information Fusion, 2020. (中科院一区TOP).

5. Wang, B. and Z. Mao, Outlier detection based on a dynamic ensemble model: Applied to process monitoring. Information Fusion, 2019. (中科院一区TOP).

6. Wang, B. and Z. Mao, Outlier detection based on Gaussian process with application to industrial processes. Applied Soft Computing, 2019. (中科院一区TOP).

7. Wang, B., Z. Mao, and K. Huang, Detecting outliers for complex nonlinear systems with dynamic ensemble learning. Chaos Solitons & Fractals, 2019. (中科院一区).

8. Wang, B., Z. Mao, and K. Huang, Detecting outliers in complex nonlinear systems controlled by predictive control strategy. Chaos, Solitons & Fractals, 2017. (中科院一区).

9. Wang, B. and Z. Mao, Detecting outliers in industrial systems using a hybrid ensemble scheme. Neural Computing & Applications, 2020. (JCR Q1中科院二区).

10. Wang, B., et al., A robust novelty detection framework based on ensemble learning. International Journal of Machine Learning and Cybernetics, 2022. (JCR Q1中科院三区).

11. Wang, B. and Z. Mao, Integrating Mach Number Prediction with Outlier Detection for Wind Tunnel Systems. Journal of Aerospace Engineering, 2019. (JCR Q2中科院四区).

12. Wang, B. and Z. Mao, One-class classifiers ensemble based anomaly detection scheme for process control systems. Transactions of the Institute of Measurement and Control, 2018. (JCR Q3中科院四区).

13. Wang, B. and Z. Mao, Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data. Measurement & Control, 2018. (JCR Q3中科院四区).

获奖情况:

1. 2024年沈阳航空航天大学优秀青年教师

2. 2023年沈阳航空航天大学教师教学创新大赛,三等奖。

3. 2024年沈阳航空航天大学青年教师教学竞赛,优秀奖。